Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "16"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 16 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 26 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 26 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 16, Node N01:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2460009 digital_ok 100.00% 100.00% 0.00% 0.00% - - 12.044256 -0.660832 13.006209 0.898091 7.248615 2.361484 0.784237 3.850205 0.0321 0.5851 0.4518 nan nan
2460008 digital_ok 100.00% 100.00% 0.00% 0.00% - - 14.536687 -0.786442 14.238769 1.174279 6.585728 1.937926 4.432744 2.108433 0.0343 0.6308 0.4941 nan nan
2460007 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.778180 -0.693338 11.136699 1.116891 5.859862 2.032185 1.484705 2.927536 0.0323 0.5941 0.4594 nan nan
2459999 digital_ok 0.00% 99.83% 99.83% 0.00% - - nan nan nan nan nan nan nan nan 0.3109 0.2321 0.2193 nan nan
2459998 digital_ok 100.00% 100.00% 0.00% 0.00% - - 9.189732 -0.481157 9.526580 0.895096 7.856871 2.597585 0.721616 2.289179 0.0309 0.6044 0.4822 nan nan
2459997 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.072731 -0.745706 10.093652 0.901560 7.625719 1.808856 1.674305 3.777152 0.0331 0.6195 0.5023 nan nan
2459996 digital_ok 100.00% 100.00% 0.00% 0.00% - - 11.174616 -0.223251 12.665316 0.993484 7.219150 2.156950 0.462620 0.361633 0.0318 0.6306 0.5050 nan nan
2459995 digital_ok 100.00% 100.00% 0.00% 0.00% - - 11.381308 -0.808614 11.766373 0.697426 7.901465 2.882777 0.302262 1.389181 0.0356 0.6215 0.4932 nan nan
2459994 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.887296 -0.885233 10.157821 0.956839 7.696881 1.941122 0.210789 2.344336 0.0318 0.6128 0.4853 nan nan
2459993 digital_ok 100.00% 100.00% 0.00% 0.00% - - 12.060718 -0.671009 9.441064 0.796616 10.066583 2.015550 0.778634 2.749475 0.0290 0.6168 0.4647 nan nan
2459991 digital_ok 100.00% 100.00% 0.00% 0.00% - - 12.901976 -1.104856 10.006926 0.897450 9.081576 2.370310 0.262071 2.428141 0.0312 0.6170 0.4910 nan nan
2459990 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.455883 -0.813173 9.802132 0.894502 8.996139 2.113281 0.175375 2.347402 0.0330 0.6169 0.4922 nan nan
2459989 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.227324 -0.940379 8.722434 1.042046 7.937637 2.003082 -0.021454 1.687581 0.0303 0.6157 0.4914 nan nan
2459988 digital_ok 100.00% 100.00% 0.00% 0.00% - - 12.267509 -0.968527 10.108349 0.760986 10.707657 1.647545 0.093406 1.927982 0.0304 0.6096 0.4957 nan nan
2459987 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.128066 -0.728777 9.797077 0.794521 6.327234 1.771893 0.755795 1.629786 0.0330 0.6224 0.5014 nan nan
2459986 digital_ok 100.00% 100.00% 0.00% 0.00% - - 12.659079 -0.873260 10.732571 0.735956 9.289405 2.081587 5.434396 1.894610 0.0317 0.6444 0.5252 nan nan
2459985 digital_ok 100.00% 100.00% 0.00% 0.00% - - 11.624792 -0.738201 9.941806 0.725028 7.158202 1.680595 1.044360 5.354158 0.0316 0.6204 0.5028 nan nan
2459984 digital_ok 100.00% 100.00% 0.00% 0.00% - - 11.086540 -0.571643 10.304240 0.612398 9.384578 1.290409 1.962227 1.204743 0.0342 0.6379 0.5214 nan nan
2459983 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.837405 -0.849476 9.877306 0.745004 9.187044 1.539856 2.781101 1.884019 0.0332 0.6531 0.5317 nan nan
2459982 digital_ok 100.00% 100.00% 0.00% 0.00% - - 9.076134 -0.098187 8.382339 0.877948 4.464968 1.238859 2.375547 0.741837 0.0316 0.6923 0.5659 nan nan
2459981 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.071387 -0.779054 10.533129 0.748620 10.340191 1.837802 0.226078 2.416912 0.0336 0.6233 0.5034 nan nan
2459980 digital_ok 100.00% 100.00% 0.00% 0.00% - - 9.888982 -0.924682 9.461399 0.703562 8.932414 1.539624 5.124936 1.843725 0.0330 0.6674 0.5471 nan nan
2459979 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.274167 -0.974578 8.768679 0.751454 8.857285 1.700243 0.422406 2.202666 0.0335 0.6194 0.5104 nan nan
2459978 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.396956 -0.791062 9.523323 0.744143 9.260345 1.489498 -0.056623 2.614082 0.0301 0.6206 0.5048 nan nan
2459977 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.689679 -0.802756 9.349987 0.692462 9.183711 2.051908 0.723376 2.466724 0.0338 0.5810 0.4701 nan nan
2459976 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.611062 -0.846896 9.843137 0.736152 9.318771 2.377564 0.686156 1.299588 0.0309 0.6277 0.5075 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 16: 2460009

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Power 13.006209 12.044256 -0.660832 13.006209 0.898091 7.248615 2.361484 0.784237 3.850205

Antenna 16: 2460008

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 14.536687 -0.786442 14.536687 1.174279 14.238769 1.937926 6.585728 2.108433 4.432744

Antenna 16: 2460007

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Power 11.136699 10.778180 -0.693338 11.136699 1.116891 5.859862 2.032185 1.484705 2.927536

Antenna 16: 2459999

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
16 N01 digital_ok nn Shape nan nan nan nan nan nan nan nan nan

Antenna 16: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Power 9.526580 9.189732 -0.481157 9.526580 0.895096 7.856871 2.597585 0.721616 2.289179

Antenna 16: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Power 10.093652 10.072731 -0.745706 10.093652 0.901560 7.625719 1.808856 1.674305 3.777152

Antenna 16: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Power 12.665316 11.174616 -0.223251 12.665316 0.993484 7.219150 2.156950 0.462620 0.361633

Antenna 16: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Power 11.766373 11.381308 -0.808614 11.766373 0.697426 7.901465 2.882777 0.302262 1.389181

Antenna 16: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 10.887296 10.887296 -0.885233 10.157821 0.956839 7.696881 1.941122 0.210789 2.344336

Antenna 16: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 12.060718 12.060718 -0.671009 9.441064 0.796616 10.066583 2.015550 0.778634 2.749475

Antenna 16: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 12.901976 12.901976 -1.104856 10.006926 0.897450 9.081576 2.370310 0.262071 2.428141

Antenna 16: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 10.455883 -0.813173 10.455883 0.894502 9.802132 2.113281 8.996139 2.347402 0.175375

Antenna 16: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 10.227324 -0.940379 10.227324 1.042046 8.722434 2.003082 7.937637 1.687581 -0.021454

Antenna 16: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 12.267509 -0.968527 12.267509 0.760986 10.108349 1.647545 10.707657 1.927982 0.093406

Antenna 16: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 10.128066 10.128066 -0.728777 9.797077 0.794521 6.327234 1.771893 0.755795 1.629786

Antenna 16: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 12.659079 -0.873260 12.659079 0.735956 10.732571 2.081587 9.289405 1.894610 5.434396

Antenna 16: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 11.624792 -0.738201 11.624792 0.725028 9.941806 1.680595 7.158202 5.354158 1.044360

Antenna 16: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 11.086540 11.086540 -0.571643 10.304240 0.612398 9.384578 1.290409 1.962227 1.204743

Antenna 16: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 10.837405 10.837405 -0.849476 9.877306 0.745004 9.187044 1.539856 2.781101 1.884019

Antenna 16: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 9.076134 9.076134 -0.098187 8.382339 0.877948 4.464968 1.238859 2.375547 0.741837

Antenna 16: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Power 10.533129 -0.779054 10.071387 0.748620 10.533129 1.837802 10.340191 2.416912 0.226078

Antenna 16: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 9.888982 -0.924682 9.888982 0.703562 9.461399 1.539624 8.932414 1.843725 5.124936

Antenna 16: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 10.274167 10.274167 -0.974578 8.768679 0.751454 8.857285 1.700243 0.422406 2.202666

Antenna 16: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 10.396956 -0.791062 10.396956 0.744143 9.523323 1.489498 9.260345 2.614082 -0.056623

Antenna 16: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 10.689679 10.689679 -0.802756 9.349987 0.692462 9.183711 2.051908 0.723376 2.466724

Antenna 16: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
16 N01 digital_ok ee Shape 10.611062 -0.846896 10.611062 0.736152 9.843137 2.377564 9.318771 1.299588 0.686156
In [ ]: